Occurrence, Severity, Risks: The Case of Wabi Shebele River Basin

217 Pages·2009·12.89 MB·English

by  Adane Abebe Awass

Hydrological Drought
Analysis - Occurrence, Severity, Risks:
The Case of Wabi Shebele River Basin,
Ethiopia
Zur Erlangung des akademischen Grades eines
Doktors der Ingenieurwissenschaften
(Dr.-Ing.)
vom Fachbereich Bauingenieurwesen
der Universita¨t Siegen
genehmigte
Dissertation
von
Adane Abebe Awass, M.Sc
Referent Univ. Prof. Dr.-Ing Gerd F¨orch, Universita¨t Siegen
Korreferent Univ. Prof. Dr.-Ing habil. Hans-B. Horlacher, TU Dresden
Tag der mu¨ndlichen Pru¨fung 16. Januar 2009
Siegen 2009

To
Victims of hydrological extremes in the region.
“When the well is dry, we know the worth of water.”
Benjamin Franklin (1706-1790)
i

Acknowledgements
This research work is financially supported mainly from Gessellschaft fur Technische
Zusammenarbeit/German Technical Cooperation (GTZ) under the framework of the
supportprogramtotheWaterTechnologyInstituteinArbaMinchUniversity, Ethiopia.
The financial support for the last year is obtained from Universitat Siegen through the
ACP-EU water facility programme. I would like to express my great gratitude to all
of them for making the fund availble. Thanks are also due to Deutscher Acadamischer
Austauschdienst(DAAD) for the scholarship and supports I obtained.
I would like to thank my supervisor Prof. Dr.-Ing Gerd F¨orch for his invaluable ad-
vices and supports throughout the research period. I thank Prof. Dr.-Ing. Hans-B.
Horlacher (TU Dresden) for his continuous very helpful comments. I acknowledge the
administrative support I obtained from Dr. Stefan Thiemann, Dipl.-Ing. Ingrid Althoff
and Dipl.-Geecol. Beate B¨ohme, all members of the Research Institute for Water and
Environment, Universitat Siegen.
I am grateful to the dendrochronology group at Wageningen University, the Netherlands
for hosting and supporting the 2-month laboratory work. I am thankful to the staffs
at the center for ecosystems studies, Wageningen University for the great experience
and social atmosphere. Particularly, I would like to extend my appreciation to Dr. Ute
Sass-Klaassen for all the help. Thanks Ute! A multitude of experts with whom I had
the opportunity to discuss my views during the course of the research helped me while I
was writing this dissertation. I would like to express my deepest appreciation and heart
felt thanks to all who gave their time so generously.
I am thankful to the hydrology section of Ministry of Water Resources and National
Meteorological Services, Ethiopia for the provision of streamflow and Meteorological
data respectively. I am thankful to Arba Minch University for providing the facilities
during my fieldwork in Ethiopia.
I would like to thank Kassa Tadele for sharing all the fun and sorrow during my stay in
Siegen. Last but by no means least I would like to convey a heartfelt gratitude to my
family and friends for their unfailing support.
Adane Abebe Awass,
Universit¨at Siegen, Germany, June 2008
iii
Abstract
by Adane Abebe Awass
This study analyses hydrological drought with due emphasis to ungauged catchments.
Identificationofhydrologicaldroughtandmethodsofunveilingitsintrinsicmulti-variate
characteristics are investigated. The severity of drought has been examined using a
multitude of methodologies. Quite often, absence of recorded long time streamflow
data hinders a reliable drought analysis and understanding of the phenomenon in the
past. Signatures of water stress are imprinted on tree rings. In this study streamflow
reconstruction is achieved by coalescing proxy data from riparian tree rings and climatic
indices. The generated data are used for extracting the multivariate features of this
extreme hydrological event. The case study is demonstrated in Wabi Shebele river basin
in Ethiopia. The methods proposed here are applicable to other similar river basins.
Extracting hydrological drought entailed defining a possible threshold level. Threshold
levels of low exceedance probability are found to be appropriate unlike the commonly
used higher exceedance probabilities in temperate climate. It was corroborated that the
frequency of some notable recent droughts as revealed with data generated from proxy
records well matches that of instrumental data. Stochastic simulation of hydrological
droughtisdoneusingSARIMAmodelsfromtimeseriesofinstrumentalmonthlystream-
flowrecordsinthestudyarea. Thenonlineardependencybetweenseverityandduration
of hydrological drought is studied using copula models. Different copula families and
parameter estimation techniques are evaluated. Joint and conditional probabilities of
severity and duration of drought in the area is specified from the derived relationship to
provide empirical insight on the nature of the extreme events. It is demonstrated that
droughts have distinct spatial patterns regarding temporal evolution and variability in
the study area in which the geographic and climatic characteristics have a high degree
of diversity. The relative strengths in association between the climatic, morphometric
and geologic features of the catchment to the base flow estimates are weighted and a
plausible relationship is produced. However, in view of the tremendous spatio-temporal
heterogeneityofclimaticandlandscapepropertiesextrapolationofresponseinformation
or knowledge from gauged to ungauged basins remains fraught with considerable diffi-
culties and uncertainties. Catchment characteristics can be related to low flows thus are
used to delineate hydrologically homogeneous pools. Severity-area-frequency analysis of
droughtintheareausingnonparametrickernelsshowshighvariabilityofdroughtevents
within the pools formed. Associated relative risks of drought are also scrutinized using
multicriteria analysis. No single variable is sufficient to portray the complexity of the
vulnerability of an area to drought.
Contents
Acknowledgements iii
Abstract iv
List of Figures ix
List of Tables xiii
Acronyms and Abbreviations xv
1 Introduction 1
1.1 Background and Statement of the Problem . . . . . . . . . . . . . . . . . 1
1.2 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Scope of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Conceptual Framework of the Research . . . . . . . . . . . . . . . . . . . 6
1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Description of Study Area and Data 9
2.1 General Physiographic Condition . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Location, Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.2 Topographic Features . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.3 Geologic Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.4 Land Use/Land Cover . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.5 Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Hydro Climatic Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Climatic Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 Hydrological Situation . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Socio-economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 Population and Settlement . . . . . . . . . . . . . . . . . . . . . . 19
2.3.2 Water Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.3 Major Drought Episodes . . . . . . . . . . . . . . . . . . . . . . . . 20
3 Characterising Hydrological Drought 23
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 Genesis of the Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3 Drought Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.1 Standardized Precipitation Index . . . . . . . . . . . . . . . . . . . 27
3.3.2 Normalized Difference Vegetation Index . . . . . . . . . . . . . . . 30
v
Contents
3.3.3 Palmer Drought Severity Index . . . . . . . . . . . . . . . . . . . . 32
3.3.4 Surface Water Supply Index . . . . . . . . . . . . . . . . . . . . . . 36
3.3.5 Multivariate Aggregate Drought Index . . . . . . . . . . . . . . . . 37
3.3.5.1 Background. . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.5.2 Data and Methodology . . . . . . . . . . . . . . . . . . . 37
3.3.5.3 Aggregate Drought Index . . . . . . . . . . . . . . . . . . 38
3.3.5.4 Results and Discussion . . . . . . . . . . . . . . . . . . . 39
3.4 Hydrologic Drought Event Definition . . . . . . . . . . . . . . . . . . . . . 41
3.4.1 Categories of Hydrological Drought . . . . . . . . . . . . . . . . . . 41
3.4.2 Drought Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4.3 Assessing Threshold Levels . . . . . . . . . . . . . . . . . . . . . . 44
4 Hydrologically Homogeneous Drought Pools 47
4.1 Catchment Characteristics as Predictors of Base Flow Index . . . . . . . . 49
4.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.1.2 Data and Methodology . . . . . . . . . . . . . . . . . . . . . . . . 49
4.1.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.1.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.2 Pooled Estimates of Low Flow Quantiles . . . . . . . . . . . . . . . . . . . 56
4.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.2 Data and Methodology . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.3 Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.4 Formation of Homogeneous Pools at Basin Scale . . . . . . . . . . . . . . 64
4.4.1 Delineation of the Hydrological Pools . . . . . . . . . . . . . . . . 64
4.4.2 Homogeneity of Pool Tests . . . . . . . . . . . . . . . . . . . . . . 66
5 Spatial Patterns of Hydrological Drought 71
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 Data and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2.1 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . 73
5.2.2 Wavelet Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.3 Area Coverage of Recent Worst Droughts . . . . . . . . . . . . . . . . . . 80
5.4 Severity-area-frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.4.1 Kernel Density Estimation . . . . . . . . . . . . . . . . . . . . . . 84
5.4.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 86
6 Paleohydrology 89
6.1 Background on Proxy Data . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.2 Dendrochronologic Records . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.3 Reconstructing Streamflows . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.3.1 Disaggregation of Annual Flows. . . . . . . . . . . . . . . . . . . . 97
6.3.2 Hydrological Drought Variables . . . . . . . . . . . . . . . . . . . . 99
6.3.2.1 Comparison with Instrumental Data . . . . . . . . . . . . 99
6.3.2.2 Univariate Drought Frequency . . . . . . . . . . . . . . . 100
7 Stochastic Simulation of Severity of Drought 103
7.1 Trends of Drought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
vi
Contents
7.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7.3 Data and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7.3.1 Time Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.4.1 Akaike Information Criterion (AIC) . . . . . . . . . . . . . . . . . 112
7.4.2 Schwarz Bayesian Criterion (SBC) . . . . . . . . . . . . . . . . . . 112
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
8 Double Chain Markov Model 115
8.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
8.2 Homogeneous Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . 117
8.2.1 Nth order Markov Model . . . . . . . . . . . . . . . . . . . . . . . 119
8.3 Non-homogeneous Markov Model . . . . . . . . . . . . . . . . . . . . . . . 120
8.4 Hidden Markov Model (HMM) . . . . . . . . . . . . . . . . . . . . . . . . 121
8.5 Double Chain Markov Model (DCMM) . . . . . . . . . . . . . . . . . . . . 122
8.5.1 Maximum-likelihood Estimation . . . . . . . . . . . . . . . . . . . 123
9 Prediction of Risks of Hydrological Drought 125
9.1 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
9.1.1 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 127
9.2 Copula Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
9.2.1 Families of Copulas. . . . . . . . . . . . . . . . . . . . . . . . . . . 130
9.2.2 Identification of Copulas . . . . . . . . . . . . . . . . . . . . . . . . 133
9.2.3 Fitting Copulas to Hydrological Drought Variables . . . . . . . . . 133
9.2.3.1 Non-parametric Estimation . . . . . . . . . . . . . . . . . 133
9.2.3.2 Parametric Estimation . . . . . . . . . . . . . . . . . . . 134
9.2.3.3 Semi-parametric Estimation . . . . . . . . . . . . . . . . 135
9.2.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 138
9.2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
9.3 Drought Risks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
9.3.1 Implications for Water Management . . . . . . . . . . . . . . . . . 154
10 Summaries and Conclusions 157
10.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
10.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
11 References 163
A Appendix 1 177
B Appendix 2 185
C Appendix 3 195
Glossary 197
Index 197
vii